Online analytical processing (OLAP) is a computer-based technique for analyzing business data in the search for business intelligence. OLAP tools provide fast analysis of multidimensional data interactively from multiple perspectives. OLAP systems are used for report generation and are suited to ad hoc analyses. There are a number of commercially available OLAP tools including Business Objects OLAP Intelligence™ which is available from Business Objects Americas of San Jose, Calif. The data in an OLAP data source is multi-dimensional and it may be partially or fully pre-aggregated. OLAP data sources, also called cubes or hyper-cubes, provide a multi-dimensional conceptual view of data, including support for hierarchies and multiple hierarchies. The core of an OLAP system is the OLAP cube. It consists of numeric facts called measures, which are categorized by dimensions. Dimensions may be organized in hierarchies. A hierarchy consists of set of dimension members and their parent-child relation to one another. The results of an OLAP query are often displayed in a cross tabulation, or cross-tab. In a cross-tab the dimensions form the rows and columns of the matrix while the measures are the values.
The querying process for OLAP can involve, depending on the specific implementation, writing and executing a query. Multidimensional Expressions (MDX) is a query language for OLAP databases, such as Structured Query Language (SQL) is a query language for relational databases. Thus, an MDX statement can be used to query an OLAP data source for a result. The MDX statement can resemble SQL statements where one can ask for data on a row and columns from a cube. As with an SQL query, each MDX query requires a data request (e.g., the “SELECT” clause), a starting point (e.g., the “FROM” clause), and a filter (e.g., the “WHERE” clause). These and other keywords provide the tools used to extract specific portions of data from a cube for analysis.
OLAP data sources consist of numerous dimensions and measures. Due to the nature of OLAP data sources, there is a lot of redundant data and a lot of columns and rows are selected in queries of even moderate complexity. In addition, users typically include many members in their queries. Usually, instead of constructing MDX queries, complex business data is explored via an interface to a multidimensional data source that communicates with the multidimensional data source and maintains sets of members that will be queried. For example, standard interface for manipulation of OLAP cubes may be a matrix interface such as a pivot table. Even without the need to construct MDX queries, manipulation of OLAP cubes via such interfaces requires knowledge of the structure and organization of the OLAP cubes. Analysis via pivot tables involves population of row, column, and slice axes with dimensions selected from the OLAP cube. The process of identifying corresponding dimensions, dimension members, and measures and populating those to axes of pivot tables is complex and requires technical knowledge. Users may need to create connection to an OLAP cube by manually browsing dimension hierarchies to select dimensions and measures of interest, and typically OLAP knowledge is required to carry out the analysis.